Motivation

What is your dataset?

The dataset used in this notebook is the Camnaught UFO sighting dataset downloaded from kaggle: https://www.kaggle.com/datasets/camnugent/ufo-sightings-around-the-world?resource=download The dataset contains over 80.000 UFO sightings with up to 11 variables connected to the sighting. Hereunder, Time of observation, Description of aircraft, Duration of encounter and Location of sighting.

Why did you choose this/these particular dataset(s)?

There is really only a handfull of other datasets concerning UFO sightings. All the reviewed datasets were scraped from the same website (https://github.com/planetsig/ufo-reports). This particular dataset is the eldest of the bunch with the cleanest data, hence the final descision to use this dataset. The reasoning for working with UFO sightings, is partially due to interest in the subject, but more importantly, the compatibility of the data and the course. The data is suitable for all sorts of visualizations; Map visualizations, Interactive exploration and the data is periodic.

What was your goal for the end user's experience?

We want the user to discover suggestions as to why UFO's are sighted more than ever in todays age. Moreover suggestions as to why the sightings are heavily location-bound. When the end user has opened their eyes to different viewpoints, the intention is for the user to explore the data independently and form their own oppinions on the matter.

Basic stats. Let's understand the dataset better

Write about your choices in data cleaning and preprocessing

Not much datacleaning has been done on this dataset. As mentioned in the previous section, this particular dataset is the cleanest of the bunch available. Other datasets had random spaces and varying formats for datetime. There are however several NaN values present in multiple of the columns. It was decided to keep the rows containing these NaN values for any analysis that did not directly handle the specific columns where the values were present. However, when the rows are directly calculated with, the NaNs are excluded from calculation.

Some columns had varying datatypes by default. We decided to convert these columns to string columns initially. This way, it was simpler to treat all rows equally, this is particularly usefull when doing row operations which require a single datatype.

Write a short section that discusses the dataset stats, containing key points/plots from your exploratory data analysis.

In this dataset, there are over 80.000 observations scattered over the entire globe. The first task in the investigation of UFO's was to gain general knowledge of global trends and then narrow down the scope. Firstly, it was discovered that around 92% of the observations happened after the year 1990. Furthermore roughly 75% of observations were in the USA after the year 1990. It was decided to further investigate the USA after 1990 as this is where the vast majority of the observations lie.

The final narrowing of the datamass happened when we discovered that California had way more observations than any other state. To put this into perspective: Washington state, which is the state with the second most sightings has 3777 in the period 1990 to 2014, while California, which is number 1 on the list, has 8301 observations.

Data Analysis

Describe your data analysis and explain what you've learned about the dataset.

The investigation this analysis has undergone is especially tricky in terms of findings. We of course knew this going into the project. On one hand, we can view all of these UFO sightings as visits from Aliens. On the other hand, every single observation in this dataset is a human error. After all, UFOs are widely aknowledged to have never visited earth. If the latter is to be believed, then a great deal of randomness and unpredictability has been invited into the data.

The analysis has been very exploratory and corious due to the nature of the data. It often raises more questions than it answers, as it often aims to rationalize UFO sightings with other factors such as druguse and time of year.

The analysis gave a great insight

Genre

  • Which genre of data story did you use?
  • Which tools did you use from each of the 3 categories of Visual Narrative (Figure 7 in Segal and Heer). Why?
  • Which tools did you use from each of the 3 categories of Narrative Structure (Figure 7 in Segal and Heer). Why?

Visualizations

  • Explain the visualizations you've chosen.
  • Why are they right for the story you want to tell?

Discussion

  • Think critically about your creation
  • What went well?,
  • What is still missing? What could be improved?, Why?

Contributions

  • Who did what?
  • You should write (just briefly) which group member was the main responsible for which elements of the assignment. (I want you guys to understand every part of the assignment, but usually there is someone who took lead role on certain portions of the work. That's what you should explain).
  • It is not OK simply to write "All group members contributed equally".
  • Make sure that you use references when they're needed and follow academic standards.
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Date_time city state/province country UFO_shape length_of_encounter_seconds described_duration_of_encounter description date_documented latitude longitude
0 10/10/1949 20:30 san marcos tx us cylinder 2700 45 minutes This event took place in early fall around 194... 4/27/2004 29.8830556 -97.941111
1 10/10/1949 21:00 lackland afb tx NaN light 7200 1-2 hrs 1949 Lackland AFB&#44 TX. Lights racing acros... 12/16/2005 29.38421 -98.581082
2 10/10/1955 17:00 chester (uk/england) NaN gb circle 20 20 seconds Green/Orange circular disc over Chester&#44 En... 1/21/2008 53.2 -2.916667
3 10/10/1956 21:00 edna tx us circle 20 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 28.9783333 -96.645833
4 10/10/1960 20:00 kaneohe hi us light 900 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.4180556 -157.803611
... ... ... ... ... ... ... ... ... ... ... ...
80327 9/9/2013 21:15 nashville tn us light 600 10 minutes Round from the distance/slowly changing colors... 9/30/2013 36.1658333 -86.784444
80328 9/9/2013 22:00 boise id us circle 1200 20 minutes Boise&#44 ID&#44 spherical&#44 20 min&#44 10 r... 9/30/2013 43.6136111 -116.202500
80329 9/9/2013 22:00 napa ca us other 1200 hour Napa UFO&#44 9/30/2013 38.2972222 -122.284444
80330 9/9/2013 22:20 vienna va us circle 5 5 seconds Saw a five gold lit cicular craft moving fastl... 9/30/2013 38.9011111 -77.265556
80331 9/9/2013 23:00 edmond ok us cigar 1020 17 minutes 2 witnesses 2 miles apart&#44 Red & White... 9/30/2013 35.6527778 -97.477778

80332 rows × 11 columns

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80332
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80332
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1906
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Date_time city state/province country UFO_shape length_of_encounter_seconds described_duration_of_encounter description date_documented latitude longitude YD Year
0 10/10/1949 20:30 san marcos tx us cylinder 2700 45 minutes This event took place in early fall around 194... 4/27/2004 29.8830556 -97.941111 1949 20:30 1949
1 10/10/1956 21:00 edna tx us circle 20 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 28.9783333 -96.645833 1956 21:00 1956
2 10/10/1960 20:00 kaneohe hi us light 900 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.4180556 -157.803611 1960 20:00 1960
3 10/10/1961 19:00 bristol tn us sphere 300 5 minutes My father is now 89 my brother 52 the girl wit... 4/27/2007 36.595 -82.188889 1961 19:00 1961
4 10/10/1965 23:45 norwalk ct us disk 1200 20 minutes A bright orange color changing to reddish colo... 10/2/1999 41.1175 -73.408333 1965 23:45 1965
... ... ... ... ... ... ... ... ... ... ... ... ... ...
65109 9/9/2013 21:15 nashville tn us light 600 10 minutes Round from the distance/slowly changing colors... 9/30/2013 36.1658333 -86.784444 2013 21:15 2013
65110 9/9/2013 22:00 boise id us circle 1200 20 minutes Boise&#44 ID&#44 spherical&#44 20 min&#44 10 r... 9/30/2013 43.6136111 -116.202500 2013 22:00 2013
65111 9/9/2013 22:00 napa ca us other 1200 hour Napa UFO&#44 9/30/2013 38.2972222 -122.284444 2013 22:00 2013
65112 9/9/2013 22:20 vienna va us circle 5 5 seconds Saw a five gold lit cicular craft moving fastl... 9/30/2013 38.9011111 -77.265556 2013 22:20 2013
65113 9/9/2013 23:00 edmond ok us cigar 1020 17 minutes 2 witnesses 2 miles apart&#44 Red & White... 9/30/2013 35.6527778 -97.477778 2013 23:00 2013

65114 rows × 13 columns

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Text(0, 0.5, 'Total sightings')
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Make this Notebook Trusted to load map: File -> Trust Notebook
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0       1949-10-10
1       1956-10-10
2       1960-10-10
3       1961-10-10
4       1965-10-10
           ...    
65109   2013-09-09
65110   2013-09-09
65111   2013-09-09
65112   2013-09-09
65113   2013-09-09
Name: Date, Length: 65114, dtype: datetime64[ns]
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Out[15]:
Date
1910-01-01    0.0
1910-06-01    0.0
1920-06-11    0.0
1925-12-28    0.0
1929-07-05    0.0
             ... 
2014-05-04    1.0
2014-05-05    1.0
2014-05-06    0.0
2014-05-07    3.0
2014-05-08    0.0
Name: CA, Length: 9818, dtype: float64
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Out[16]:
0       10/10/1968
1       10/10/1979
2       10/10/1989
3       10/10/1995
4       10/10/1998
           ...    
8907      9/9/2012
8908      9/9/2012
8909      9/9/2012
8910      9/9/2013
8911      9/9/2013
Name: Date, Length: 8912, dtype: object
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UFO_shape changing chevron cigar circle cone cross cylinder diamond disk egg ... formation light other oval rectangle round sphere teardrop triangle unknown
Date
1/1/1944 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1/1/1961 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1/1/1968 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1/1/1977 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1/1/1978 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
9/9/2009 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9/9/2010 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9/9/2011 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
9/9/2012 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9/9/2013 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

4664 rows × 23 columns

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{'1/1/2008': ['light', 8.0],
 '1/1/2010': ['light', 7.0],
 '1/6/2011': ['fireball', 7.0],
 '12/31/2013': ['light', 6.0],
 '7/23/2002': ['triangle', 6.0],
 '7/4/2004': ['light', 8.0],
 '7/4/2009': ['light', 6.0],
 '7/7/2000': ['light', 8.0],
 '8/30/2003': ['light', 6.0]}
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Make this Notebook Trusted to load map: File -> Trust Notebook
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Make this Notebook Trusted to load map: File -> Trust Notebook
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state/province AK AL AR AZ CA CO CT DC DE FL ... SD TN TX UT VA VT WA WI WV WY
Year
1910 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1920 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1925 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1929 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1931 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2010 10.0 23.0 34.0 97.0 516.0 56.0 55.0 1.0 9.0 190.0 ... 14.0 54.0 202.0 34.0 68.0 13.0 238.0 51.0 14.0 11.0
2011 16.0 33.0 38.0 138.0 541.0 100.0 66.0 2.0 11.0 282.0 ... 14.0 86.0 224.0 44.0 87.0 20.0 263.0 100.0 22.0 17.0
2012 42.0 51.0 51.0 177.0 639.0 103.0 138.0 4.0 27.0 378.0 ... 25.0 111.0 224.0 79.0 123.0 50.0 366.0 140.0 58.0 11.0
2013 44.0 68.0 47.0 196.0 622.0 126.0 114.0 0.0 12.0 454.0 ... 16.0 110.0 212.0 71.0 175.0 59.0 286.0 158.0 48.0 13.0
2014 23.0 43.0 8.0 98.0 265.0 39.0 19.0 0.0 5.0 223.0 ... 3.0 41.0 66.0 29.0 51.0 6.0 92.0 29.0 22.0 3.0

83 rows × 52 columns

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Index(['1949', '1956', '1960', '1961', '1965', '1966', '1968', '1970', '1971',
       '1972', '1973', '1974', '1975', '1976', '1977', '1978', '1979', '1980',
       '1984', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',
       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',
       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',
       '1987', '1950', '1952', '1954', '1957', '1959', '1967', '1969', '1981',
       '1982', '1983', '1985', '1986', '1936', '1943', '1953', '1955', '1962',
       '1964', '1958', '1963', '1947', '2014', '1910', '1944', '1951', '1948',
       '1945', '1925', '1946', '1931', '1920', '1939', '1941', '1942', '1937',
       '1929', '1934'],
      dtype='object', name='years')
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Name Geography Type Year Resident Population Percent Change in Resident Population Resident Population Density Resident Population Density Rank Number of Representatives Change in Number of Representatives Average Apportionment Population Per Representative
0 Alabama State 1910 2,138,093 16.9 42.2 25.0 10.0 1.0 213,809
1 Alaska State 1910 64,356 1.2 0.1 52.0 NaN NaN NaN
2 Arizona State 1910 204,354 66.2 1.8 49.0 NaN NaN NaN
3 Arkansas State 1910 1,574,449 20.0 30.3 30.0 7.0 0.0 224,921
4 California State 1910 2,377,549 60.1 15.3 38.0 11.0 3.0 216,051
... ... ... ... ... ... ... ... ... ... ...
679 Midwest Region Region 2020 68,985,454 3.1 NaN NaN NaN NaN NaN
680 Northeast Region Region 2020 57,609,148 4.1 NaN NaN NaN NaN NaN
681 South Region Region 2020 126,266,107 10.2 NaN NaN NaN NaN NaN
682 West Region Region 2020 78,588,572 9.2 NaN NaN NaN NaN NaN
683 United States Nation 2020 331,449,281 7.4 93.8 NaN 435.0 7.0 761,169

684 rows × 10 columns

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Name AK AL AR AZ CA CO CT DC DE FL ... SD TN TX UT VA VT WA WI WV WY
1910 2,138,093 64,356 204,354 1,574,449 2,377,549 799,024 1,114,756 202,322 331,069 752,619 ... 583,888 2,184,789 3,896,542 373,351 355,956 2,061,612 1,141,990 1,221,119 2,333,860 145,965
1920 2,348,174 55,036 334,162 1,752,204 3,426,861 939,629 1,380,631 223,003 437,571 968,470 ... 636,547 2,337,885 4,663,228 449,396 352,428 2,309,187 1,356,621 1,463,701 2,632,067 194,402
1930 2,646,248 59,278 435,573 1,854,482 5,677,251 1,035,791 1,606,903 238,380 486,869 1,468,211 ... 692,849 2,616,556 5,824,715 507,847 359,611 2,421,851 1,563,396 1,729,205 2,939,006 225,565
1940 2,832,961 72,524 499,261 1,949,387 6,907,387 1,123,296 1,709,242 266,505 663,091 1,897,414 ... 642,961 2,915,841 6,414,824 550,310 359,231 2,677,773 1,736,191 1,901,974 3,137,587 250,742
1950 3,061,743 128,643 749,587 1,909,511 10,586,223 1,325,089 2,007,280 318,085 802,178 2,771,305 ... 652,740 3,291,718 7,711,194 688,862 377,747 3,318,680 2,378,963 2,005,552 3,434,575 290,529
1960 3,266,740 226,167 1,302,161 1,786,272 15,717,204 1,753,947 2,535,234 446,292 763,956 4,951,560 ... 680,514 3,567,089 9,579,677 890,627 389,881 3,966,949 2,853,214 1,860,421 3,951,777 330,066
1970 3,444,165 300,382 1,770,900 1,923,295 19,953,134 2,207,259 3,031,709 548,104 756,510 6,789,443 ... 665,507 3,923,687 11,196,730 1,059,273 444,330 4,648,494 3,409,169 1,744,237 4,417,731 332,416
1980 3,893,888 401,851 2,718,215 2,286,435 23,667,902 2,889,964 3,107,576 594,338 638,333 9,746,324 ... 690,768 4,591,120 14,229,191 1,461,037 511,456 5,346,818 4,132,156 1,949,644 4,705,767 469,557
1990 4,040,587 550,043 3,665,228 2,350,725 29,760,021 3,294,394 3,287,116 666,168 606,900 12,937,926 ... 696,004 4,877,185 16,986,510 1,722,850 562,758 6,187,358 4,866,692 1,793,477 4,891,769 453,588
2000 4,447,100 626,932 5,130,632 2,673,400 33,871,648 4,301,261 3,405,565 783,600 572,059 15,982,378 ... 754,844 5,689,283 20,851,820 2,233,169 608,827 7,078,515 5,894,121 1,808,344 5,363,675 493,782
2010 4,779,736 710,231 6,392,017 2,915,918 37,253,956 5,029,196 3,574,097 897,934 601,723 18,801,310 ... 814,180 6,346,105 25,145,561 2,763,885 625,741 8,001,024 6,724,540 1,852,994 5,686,986 563,626
2020 5,024,279 733,391 7,151,502 3,011,524 39,538,223 5,773,714 3,605,944 989,948 689,545 21,538,187 ... 886,667 6,910,840 29,145,505 3,271,616 643,077 8,631,393 7,705,281 1,793,716 5,893,718 576,851

12 rows × 52 columns

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BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('x', 12), ('y', 83)
BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('x', 12), ('y', 83)
BokehUserWarning: ColumnDataSource's columns must be of the same length. Current lengths: ('x', 12), ('y', 83)
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'C:\\Users\\Mikkel\\Desktop\\2. semester\\Tirsdag - 02806 Social data analysis and visualization\\Untitled Folder\\Interactive.html'
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Date_time city state/province country UFO_shape length_of_encounter_seconds described_duration_of_encounter description date_documented latitude longitude YD Year
0 10/10/1949 20:30 san marcos tx us cylinder 2700 45 minutes This event took place in early fall around 194... 4/27/2004 29.8830556 -97.941111 1949 20:30 1949
1 10/10/1949 21:00 lackland afb tx NaN light 7200 1-2 hrs 1949 Lackland AFB&#44 TX. Lights racing acros... 12/16/2005 29.38421 -98.581082 1949 21:00 1949
2 10/10/1955 17:00 chester (uk/england) NaN gb circle 20 20 seconds Green/Orange circular disc over Chester&#44 En... 1/21/2008 53.2 -2.916667 1955 17:00 1955
3 10/10/1956 21:00 edna tx us circle 20 1/2 hour My older brother and twin sister were leaving ... 1/17/2004 28.9783333 -96.645833 1956 21:00 1956
4 10/10/1960 20:00 kaneohe hi us light 900 15 minutes AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.4180556 -157.803611 1960 20:00 1960
... ... ... ... ... ... ... ... ... ... ... ... ... ...
80327 9/9/2013 21:15 nashville tn us light 600 10 minutes Round from the distance/slowly changing colors... 9/30/2013 36.1658333 -86.784444 2013 21:15 2013
80328 9/9/2013 22:00 boise id us circle 1200 20 minutes Boise&#44 ID&#44 spherical&#44 20 min&#44 10 r... 9/30/2013 43.6136111 -116.202500 2013 22:00 2013
80329 9/9/2013 22:00 napa ca us other 1200 hour Napa UFO&#44 9/30/2013 38.2972222 -122.284444 2013 22:00 2013
80330 9/9/2013 22:20 vienna va us circle 5 5 seconds Saw a five gold lit cicular craft moving fastl... 9/30/2013 38.9011111 -77.265556 2013 22:20 2013
80331 9/9/2013 23:00 edmond ok us cigar 1020 17 minutes 2 witnesses 2 miles apart&#44 Red & White... 9/30/2013 35.6527778 -97.477778 2013 23:00 2013

80332 rows × 13 columns

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Out[33]:
country au ca de gb us
Year
1910 0.0 0.0 0.0 0.0 2.0
1920 0.0 0.0 0.0 0.0 1.0
1925 0.0 0.0 0.0 0.0 1.0
1929 0.0 0.0 0.0 0.0 1.0
1931 0.0 0.0 0.0 0.0 2.0
... ... ... ... ... ...
2010 16.0 137.0 4.0 115.0 3548.0
2011 13.0 127.0 3.0 51.0 4379.0
2012 19.0 242.0 6.0 82.0 6320.0
2013 32.0 248.0 6.0 48.0 6056.0
2014 14.0 45.0 3.0 21.0 1964.0

83 rows × 5 columns

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C:\Users\Mikkel\AppData\Local\Temp\ipykernel_5560\4244762704.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

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Day_of_week
Day_of_week
Monday 10064
Tuesday 10789
Wednesday 10980
Thursday 11030
Friday 11606
Saturday 14066
Sunday 11797
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